The Client Strēm is a context-aware AI investment research platform for analysts, portfolio managers, and sector researchers. It was founded by Torrence Jennette (investment professional) and Mallory Musante (marketing analytics expert). The MVP Goal In Part 1, the team completed a full discovery phase: persona research, feature mapping, system architecture, and a Figma prototype. Now at this stage we had to bring this prototype to life. We worked in two-week cycles with regular client demos to keep priorities aligned. The MVP had to handle financial data, answer research questions, and be tested by actual users.
MVP Development: 3 Key Stages Building the MVP came down to 3 key stages. Each one was necessary to bring together a platform that combines a multi-agent AI system with an automated data pipeline monitoring 3,000+ US securities daily. 1. Data Infrastructure – we built an automated pipeline connecting four data sources: financials, stock prices, macro indicators, and regulatory filings, covering 3,000+ US companies. 2. AI Agent System – rather than one model doing everything, specialized agents work in parallel: one tracks news, one checks financials, one handles valuations. A lead agent coordinates the team and delivers a single, combined answer. 3. Platform and Core Workflows – one unified workspace replaces fragmented dashboards. Built around a central chat, it includes a company dashboard with filings and news, a cross-company news feed, watchlist and coverage management, and report export to PDF and Excel.
3 Key Features The first MVP iteration is a working prototype of a multi-agent investment research platform designed to help analysts work faster and access more valuable information. It features a minimalistic design with three prioritized core features. The goal is to test the prototype as soon as possible and improve functionality based on user feedback.
"Research" to quickly find relevant info At the center of the platform is a natural language interface that acts as a specialized financial assistant. Analysts ask questions in plain English, and the AI pulls from real-time financial data, SEC filings, and news sources to deliver precise, citation-backed answers. This module directly connects raw data to instant insights, minimizing routine research time.
"Coverage" to add context to your research This is where Strēm separates itself from generic AI tools. The platform doesn’t just process isolated queries; it acts as a context-aware intelligence system. Analysts can build a Coverage list by tracking specific companies, assigning strategic positions, and adding personal investment notes. The AI remembers this context, allowing teams to run retrospectives on past predictions and compare positions against market benchmarks. Because this context is shared at the company level, portfolio managers and researchers can seamlessly view their teammates’ insights, transforming the platform into a collective intelligence hub. In an industry where timing and alignment matter, that shared layer of memory is what turns individual analysis into a competitive advantage.
"Alerts & Monitoring" to keep you in the context To ensure analysts never miss a critical market movement, an automated news pipeline runs continuously. News appears seamlessly within the active Research chat, inside dedicated company dashboards, and in a standalone feed. The Alerts system is tied directly to the user’s Coverage list and proactively notifies them only when highly relevant events impact the specific securities they are tracking. This means analysts spend less time scanning for updates and has more time to analyze them and make educated decisions.
